仅用姿态数据估算航天器惯性张量,精度远超传统方法。
Estimation of Spacecraft Inertia Tensor Using Attitude-Only Data from Torque-Free Motion

- 基于无陀螺仪的纯姿态数据,利用欧拉方程精确解与四元数映射优化
- 单弧500秒可降误差一个数量级,计算量少两个数量级
- 适用于近距离操作仿真,支持长时间高精度姿态预测
本文提出一种仅依赖无外部力矩旋转下的姿态数据,估计航天器归一化惯性张量的方法。该方法支持连续单弧观测及多段短弧联合估计,无需陀螺仪测量或已知控制力矩。通过KKT方法快速线性初始化,并采用欧拉方程的Jacobi-椭圆解与Magnus展开四元数映射进行非线性优化。在受控姿态噪声下,使用单个500秒弧段,误差较同初始条件下的扩展卡尔曼滤波(EKF)降低约一个数量级,计算量减少近两个数量级;三段各100秒弧段联合估计同样显著提升精度,且速度快于EKF一个数量级以上。基于单目图像导出的姿态进行的逼真近距离操作仿真表明:2000秒单弧案例中,惯性张量中位误差低于千分之一,支持10小时姿态预测,中位误差小于一位数度;三弧案例(每段30–300秒)性能优于EKF,表现取决于旋转激励程度与采样时间间隔。
原文摘要 · Abstract (English)
We present an attitude-only framework for estimating a spacecraft's normalized inertia tensor from torque-free rotational motion. Our method supports both continuous single-arc observations and the joint use of multiple short torque-free arcs, while requiring neither gyroscope measurements nor known control torques. A Karush-Kuhn-Tucker formulation provides a fast linear initialization, which is refined by nonlinear shooting using the exact Jacobi-elliptic solution of Euler's equations and a Magnus-expansion quaternion map. Under controlled attitude noise, tests using a single 500-second arc reduced inertia-tensor error by approximately one order of magnitude relative to an Extended Kalman Filter initialized from the same estimate, while requiring nearly two orders of magnitude less computation. Joint estimation from three 100-second arcs provided a similar improvement in accuracy and remained more than one order of magnitude faster. Photorealistic proximity-operations simulations further evaluated both strategies using monocular image-derived attitudes. The 2000-second single-arc cases achieved sub-thousandth median inertia-tensor error and supported 10-hour attitude predictions with single-digit-degree median error. In three-arc cases using 30-300 seconds per arc, our method consistently outperformed the EKF refinement, with performance governed by rotational excitation and temporal sampling.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。